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I have 2 python file which are ‘anna_phog.py’ and ‘anna_phog_demo.py’. There’s no error when I run ‘anna_phog.py’

However when I tried to run the ‘anna_phog_demo.py’ on Jupyter Notebook, I got TypeError: unsupported operand type(s) for /: ‘int’ and ‘builtin_function_or_method’

This is the error that I got :

TypeError                                 Traceback (most recent call last)
<ipython-input-28-47b447c36f03> in <module>
      1 Image = imageio.imread(image_path)
----> 2 p = anna_phog(Image, bin, angle, Level, roi)
      3 print("P: n{}".format(p))
      4 print(len(p), type(p))

~DesktopFYPanna_phog.ipynb in anna_phog(Img, bin, angle, L, roi)
     36     "        E = cv2.Canny(G,lower,upper) #high and low tresholdn",
     37     "        GradientX, GradientY = np.gradient(G)n",
---> 38     "        GradientYY = np.gradient(GradientY, axis=1)n",
     39     "        Gr = np.sqrt(np.square(GradientX)+np.square(GradientY))n",
     40     "        index = GradientX == 0n",

~DesktopFYPanna_phog.ipynb in anna_BinMatrix(A, E, G, angle, bin)
     57     "    return p"
     58    ]
---> 59   },
     60   {
     61    "cell_type": "code",

TypeError: unsupported operand type(s) for /: 'int' and 'builtin_function_or_method'

I tried to change few parts but still got the same error. I include both of the code below if anyone can help me to remove this error.

This one is anna_phog_demo.py code

from ipynb.fs.full.anna_phog import anna_phog
import imageio
import matplotlib.pyplot as plt

image_path = r"C:UsersuserDocumentsFYPCODEPHOGPHOG-mainPHOG-mainimage_0058.jpg"
S = 8
angle = 360
Level = 3
roi = [1,225,1,300]
save=True

Image = imageio.imread(image_path)
p = anna_phog(Image, bin, angle, Level, roi)
print("P: n{}".format(p))
print(len(p), type(p))

This is the anna_phog.py code

import numpy as np
import imageio
import cv2
import matplotlib.pyplot as plt

def anna_phog(Img, bin, angle, L, roi):
   
    if Img.shape[2] == 3:
        G = cv2.cvtColor(Img, cv2.COLOR_BGR2GRAY)
    else:
        G = Img

    if np.sum(G) > 100:
        # apply automatic Canny edge detection using the computed median
        sigma = 0.33
        v = np.median(G)
        lower = int(max(0, (1.0 - sigma) * v))
        upper = int(min(255, (1.0 + sigma) * v))
        E = cv2.Canny(G,lower,upper) #high and low treshold
        GradientX, GradientY = np.gradient(G)
        GradientYY = np.gradient(GradientY, axis=1)

        Gr = np.sqrt(np.square(GradientX)+np.square(GradientY))
        index = GradientX == 0
        GradientX[index] = 1e-5 #maybe another value

        YX = GradientY*GradientX
        if angle == 180: A = ((np.arctan(YX)+(np.pi/2))*180)/np.pi
        if angle == 360: A = ((np.arctan2(GradientY,GradientX)+np.pi)*180)/np.pi

        bh, bv = anna_BinMatrix(A,E,Gr,angle,bin)

    else:
        bh = np.zeros(Img.shape)
        bv = np.zeros(Img.shape)

    bh_roi = bh[roi[0]:roi[1], roi[2]:roi[3]]
    bv_roi = bv[roi[0]:roi[1], roi[2]:roi[3]]

    p = anna_PhogDescriptor(bh_roi,bv_roi,L,bin)
    return p


def anna_BinMatrix(A,E,G,angle,bin):
    
    n, contorns = cv2.connectedComponents(E, connectivity=8)

    X = E.shape[1]
    Y = E.shape[0]
    bm = np.zeros(shape=(Y,X))
    bv = np.zeros(shape=(Y,X))

    nAngle = angle/bin

    for i in range(n):
        posY, posX = np.where(contorns==i)
        for j in range(posY.shape[0]):
            pos_x = posX[j]
            pos_y = posY[j]

            b = np.ceil(A[pos_y,pos_x]/nAngle)
            if b==0: bin=1
            if G[pos_y,pos_x]>0:
                bm[pos_y,pos_x] = b
                bv[pos_y,pos_x] = G[pos_y,pos_x]

    return (bm, bv)

def anna_PhogDescriptor(bh,bv,L,bin):
   
    p = np.array([])
    #level 0
    for b in range(bin):
        ind = bh==b
        p = np.append(p, np.sum(bv[ind]))

    #higher levels
    for l in range(1, L+1):
        x = int(np.trunc(bh.shape[1]/(2**l)))
        y = int(np.trunc(bh.shape[0]/(2**l)))
        for xx in range(0, bh.shape[1]-x+1, x):
            for yy in range(0, bh.shape[0]-y+1, y):
                print(l)
                bh_cella = bh[yy:yy+y, xx:xx+x]
                bv_cella = bv[yy:yy+y, xx:xx+x]

                for b in range(bin):
                    ind = bh_cella==b
                    p = np.append(p, np.sum(bv_cella[ind], axis=0))

    if np.sum(p)!=0:
        p = p/np.sum(p)

    return p
furas Answered question June 11, 2021